OpenRaiser/Intern-Atlas huggingface dataset:
Reinforcement Learning for Tool-Integrated Interleaved Thinking towards Cross-Domain Generalization
Venue: arXiv cs.LG 2026
Year: 2026

Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in reasoning and tool utilization. However, the generalization of tool-augmented reinforcement learning (RL) across diverse domains remains a significant challenge. Standard paradigms often treat tool usage as a linear or isolated event, which becomes brittle when transferring skills from restricted domains (e.g., mathematics) to open-ended tasks. In this work, we investigate the cross-domain generalization of an LLM agent trained exclusively on mathematical problem-solving. To facilitate robust skill transfer, we propose a {\textbf{R}einforcement Learning for \textbf{I}nterleaved \textbf{T}ool \textbf{E}xecution (RITE)}. Unlike traditional methods, RITE enforces a continuous ``Plan-Action-Reflection'' cycle, allowing the model to ground its reasoning in intermediate tool outputs and self-correct during long-horizon tasks. To effectively train this complex interleaved policy, we introduce {Dr. GRPO}, a robust optimization objective that utilizes token-level loss aggregation with importance sampling to mitigate reward sparsity and high-variance credit assignment. Furthermore, we employ a dual-component reward system and dynamic curriculum via online rollout filtering to ensure structural integrity and sample efficiency. Extensive experiments reveal that our approach, despite being trained solely on math tasks, achieves state-of-the-art performance across diverse reasoning domains, demonstrating high token efficiency and strong generalization capabilities.
Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality
Venue: arXiv cs.CL 2026
Year: 2026

Psychometric tests are increasingly used to assess psychological constructs in large language models (LLMs). However, it remains unclear whether these tests -- originally developed for humans -- yield meaningful results when applied to LLMs. In this study, we systematically evaluate the reliability and validity of human psychometric tests on 17 LLMs for three constructs: sexism, racism, and morality. We find moderate reliability across multiple item and prompt variations. Validity is evaluated through both convergent (i.e., testing theory-based inter-test correlations) and ecological approaches (i.e., testing the alignment between tests scores and behavior in real-world downstream tasks). Crucially, we find that psychometric test scores do not align, and in some cases even negatively correlate with, model behavior in downstream tasks, indicating low ecological validity. Our results highlight that systematic evaluations of psychometric tests on LLMs are essential before interpreting their scores. Our findings also suggest that psychometric tests designed for humans cannot be applied directly to LLMs without adaptation.
Forward-Forward Autoencoder Architectures for Energy-Efficient Wireless Communications
Venue: arXiv cs.IT 2026
Year: 2026

The application of deep learning to the area of communications systems has been a growing field of interest in recent years. Forward-forward (FF) learning is an efficient alternative to the backpropagation (BP) algorithm, which is the typically used training procedure for neural networks. Among its several advantages, FF learning does not require the communication channel to be differentiable and does not rely on the global availability of partial derivatives, allowing for an energy-efficient implementation. In this work, we design end-to-end learned autoencoders using the FF algorithm and numerically evaluate their performance for the additive white Gaussian noise and Rayleigh block fading channels. We demonstrate their competitiveness with BP-trained systems in the case of joint coding and modulation, and in a scenario where a fixed, non-differentiable modulation stage is applied. Moreover, we provide further insights into the design principles of the FF network, its training convergence behavior, and significant memory and processing time savings compared to BP-based approaches.
Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model
Venue: arXiv cs.AI 2026
Year: 2026

Deductive and abductive reasoning are two critical paradigms for analyzing knowledge graphs, enabling applications from financial query answering to scientific discovery. Deductive reasoning on knowledge graphs usually involves retrieving entities that satisfy a complex logical query, while abductive reasoning generates plausible logical hypotheses from observations. Despite their clear synergistic potential, where deduction can validate hypotheses and abduction can uncover deeper logical patterns, existing methods address them in isolation. To bridge this gap, we propose DARK, a unified framework for Deductive and Abductive Reasoning in Knowledge graphs. As a masked diffusion model capable of capturing the bidirectional relationship between queries and conclusions, DARK has two key innovations. First, to better leverage deduction for hypothesis refinement during abductive reasoning, we introduce a self-reflective denoising process that iteratively generates and validates candidate hypotheses against the observed conclusion. Second, to discover richer logical associations, we propose a logic-exploration reinforcement learning approach that simultaneously masks queries and conclusions, enabling the model to explore novel reasoning compositions. Extensive experiments on multiple benchmark knowledge graphs show that DARK achieves state-of-the-art performance on both deductive and abductive reasoning tasks, demonstrating the significant benefits of our unified approach.
Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Venue: arXiv cs.RO 2026
Year: 2026

Accurate state estimation is critical for legged and aerial robots operating in dynamic, uncertain environments. A key challenge lies in specifying process and measurement noise covariances, which are typically unknown or manually tuned. In this work, we introduce a bi-level optimization framework that jointly calibrates covariance matrices and kinematic parameters in an estimator-in-the-loop manner. The upper level treats noise covariances and model parameters as optimization variables, while the lower level executes a full-information estimator. Differentiating through the estimator allows direct optimization of trajectory-level objectives, resulting in accurate and consistent state estimates. We validate our approach on quadrupedal and humanoid robots, demonstrating significantly improved estimation accuracy and uncertainty calibration compared to hand-tuned baselines. Our method unifies state estimation, sensor, and kinematics calibration into a principled, data-driven framework applicable across diverse robotic platforms.
Wireless Sensing of Temperature, Strain and Crack Growth in 3D-Printed Metal Structures via Magneto-Responsive Inclusions
Venue: arXiv cs.ET 2026
Year: 2026

This study demonstrates the first realization of wireless strain, temperature and crack growth sensing within 3D-printed metallic structures using standard electromagnetic inspection hardware. This establishes a path toward need-based maintenance for parts operating in harsh environments driven by accurate, real-time damage assessments instead of relying on regularly scheduled maintenance teardowns. To this end, we encapsulate and embed magnetoelastic and thermomagnetic materials during additive manufacturing. Mechanical and thermal stimuli affect the magnetic permeability of the embedded materials, which modulates the flux through a coil placed on or near the part's surface. We demonstrate strain sensing accurate to +/-27x10-6 and temperature sensing accurate to +/-0.75 oC. We highlight these sensors' capabilities by detecting the onset of plasticity and fatigue-driven crack growth thousands of cycles before critical failure.
Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning
Venue: arXiv cs.MA 2026
Year: 2026

Current macroeconomic models with agent heterogeneity can be broadly divided into two main groups. Heterogeneous-agent general equilibrium (GE) models, such as those based on Heterogeneous Agent New Keynesian (HANK) or Krusell-Smith (KS) approaches, rely on GE and 'rational expectations', somewhat unrealistic assumptions that make the models very computationally cumbersome, which in turn limits the amount of heterogeneity that can be modelled. In contrast, agent-based models (ABMs) can flexibly encompass a large number of arbitrarily heterogeneous agents, but typically require the specification of explicit behavioural rules, which can lead to a lengthy trial-and-error model-development process. To address these limitations, we introduce MARL-BC, a framework that integrates deep multi-agent reinforcement learning (MARL) with real business cycle (RBC) models. We demonstrate that MARL-BC can: (1) recover textbook RBC results when using a single agent; (2) recover the results of the mean-field KS model using a large number of identical agents; and (3) effectively simulate rich heterogeneity among agents, a hard task for traditional GE approaches. Our framework can be thought of as an ABM if used with a variety of heterogeneous interacting agents, and can reproduce GE results in limit cases. As such, it is a step towards a synthesis of these often opposed modelling paradigms.
Metronome: Efficient Scheduling for Periodic Traffic Jobs with Network and Priority Awareness
Venue: arXiv cs.DC 2026
Year: 2026

With the rapid growth in computing power demand, cloud native networks have emerged as a promising solution to address the challenges of efficient resource coordination, particularly in coping with the dynamic fluctuations of network bandwidth in clusters. We propose Metronome, a network-aware and priority-aware scheduling mechanism for cloud native networks. This mechanism is designed to support jobs that exhibit periodic traffic patterns and dynamic bandwidth demands, particularly in the context of distributed training. Specifically, Metronome employs a time-division multiplexing approach that leverages job traffic characteristics to construct an elastic network resource allocation model, enabling efficient bandwidth sharing across multiple jobs. In addition, it incorporates a multi-objective optimization strategy, jointly considering latency and job priorities to achieve globally optimal as well as dynamic resource allocation. Finally, Metronome adapts to the dynamic environment by monitoring the cluster and performing reconfiguration operations. Extensive experiments with 13 common machine learning models demonstrate that Metronome can enhance cluster resource utilization while guaranteeing service performance. Compared with the existing Kubernetes scheduling mechanisms across multiple scenarios, Metronome reduces job completion time by up to 19.50% while improving average bandwidth utilization by up to 23.20%.
Quantum Annealing for Staff Scheduling in Educational Environments
Venue: arXiv cs.ET 2026
Year: 2026

We address a novel staff allocation problem that arises in the organization of collaborators among multiple school sites and educational levels. The problem emerges from a real case study in a public school in Calabria, Italy, where staff members must be distributed across kindergartens, primary, and secondary schools under constraints of availability, competencies, and fairness. To tackle this problem, we develop an optimization model and investigate a solution approach based on quantum annealing. Our computational experiments on real-world data show that quantum annealing is capable of producing balanced assignments in short runtimes. These results provide evidence of the practical applicability of quantum optimization methods in educational scheduling and, more broadly, in complex resource allocation tasks.
PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Venue: arXiv cs.CL 2026
Year: 2026

Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a structured form. However, existing KH-based RAG methods suffer from three major limitations: static retrieval planning, non-adaptive retrieval execution, and superficial use of KH structure and semantics, which constrain their ability to perform effective multi-hop question answering. To overcome these limitations, we propose PRoH, a dynamic Planning and Reasoning over Knowledge Hypergraphs framework. PRoH incorporates three core innovations: (i) a context-aware planning module that sketches the local KH neighborhood to guide structurally grounded reasoning plan generation; (ii) a structured question decomposition process that organizes subquestions as a dynamically evolving Directed Acyclic Graph (DAG) to enable adaptive, multi-trajectory exploration; and (iii) an Entity-Weighted Overlap (EWO)-guided reasoning path retrieval algorithm that prioritizes semantically coherent hyperedge traversals. Experiments across multiple domains demonstrate that PRoH achieves state-of-the-art performance, surpassing the prior SOTA model HyperGraphRAG by an average of 19.73% in F1 and 8.41% in Generation Evaluation (G-E) score, while maintaining strong robustness in long-range multi-hop reasoning tasks.
Proof of Cloud: Data Center Execution Assurance for Confidential VMs
Venue: arXiv cs.CR 2026
Year: 2026

Confidential Virtual Machines (CVMs) protect data in use by running workloads within hardware-enforced Trusted Execution Environments (TEEs). However, existing CVM attestation mechanisms only certify what code is running, not where it is running. Commercial TEEs mitigate passive physical attacks through memory encryption but explicitly exclude active hardware tampering (memory interposers, physical side channels, ...). Yet current attestations provide no cryptographic evidence that a CVM executes on hardware residing within a trusted data center where such attacks would not take place. This gap enables proxy attacks in which valid attestations are combined across machines to falsely attest trusted execution. To bridge this gap, we introduce Data Center Execution Assurance (DCEA), a design that generates a cryptographic Proof of Cloud by binding CVM attestation to platform-level Trusted Platform Module (TPM) evidence. DCEA combines two independent roots of trust. First, the TEE manufacturer, and second, the infrastructure provider, by cross-linking runtime TEE measurements with the vTPM-measured boot CVM state. This binding ensures that CVM execution, vTPM quotes, and platform provenance all originate from the same physical chassis. We formalize the environment's provenance and show that DCEA prevents advanced relay attacks, including a novel mix-and-match proxy attack. Using the AGATE framework in the Universal Composability model, we prove that DCEA emulates an ideal location-aware TEE even under a malicious host software stack. We implement DCEA on Google Cloud bare-metal Intel TDX instances using Intel TXT and evaluate its performance, demonstrating practical overheads and deployability. DCEA refines the CVM threat model and enables verifiable execution-location guarantees for privacy-sensitive workloads.
Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space
Venue: arXiv cs.CV 2026
Year: 2026

Multimodal reasoning aims to enhance the capabilities of MLLMs by incorporating intermediate reasoning steps before reaching the final answer. It has evolved from text-only reasoning to the integration of visual information, enabling the thought process to be conveyed through both images and text. Despite its effectiveness, current multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. To address these issues, we introduce multimodal latent reasoning with the advantages of multimodal representation, reduced annotation, and inference efficiency. To facilitate it, we propose Interleaved Vision-Text Latent Reasoning (IVT-LR), which injects both visual and textual information in the reasoning process within the latent space. Specifically, IVT-LR represents each reasoning step by combining two implicit parts: latent text (the hidden states from the previous step) and latent vision (a set of selected image embeddings). We further introduce a progressive multi-stage training strategy to enable MLLMs to perform the above multimodal latent reasoning steps. Experiments on M$^3$CoT and ScienceQA demonstrate that our IVT-LR method achieves an average performance increase of 5.45\% in accuracy, while simultaneously achieving a speed increase of over 5 times compared to existing approaches.
Characterizing Agent-Based Model Dynamics via $ε$-Machines and Kolmogorov-Style Complexity
Venue: arXiv cs.MA 2026
Year: 2026

We propose a two-level information-theoretic framework for characterizing the informational organization of Agent-Based Model (ABM) dynamics within the broader paradigm of Complex Adaptive Systems (CAS). At the macro level, a pooled $\varepsilon$-machine is reconstructed as a reference model summarizing the system-wide informational regime. At the micro level, $\varepsilon$-machines are reconstructed for each caregiver--elder dyad and variable, complemented by algorithm-agnostic Kolmogorov-style measures, including normalized LZ78 complexity and bits per symbol from lossless compression. The resulting feature set, $\{h_μ, C_μ, E, \mathrm{LZ78}, \mathrm{bps}\}$, enables distributional analysis, stratified comparisons, and unsupervised clustering across agents and scenarios. Empirical results show that coupling $\varepsilon$-machines with compression diagnostics yields a coherent picture of where predictive information resides in the caregiving ABM. Global reconstructions provide a memoryless baseline ($L{=}0$ under coarse symbolizations), whereas per-dyad models reveal localized structure, particularly for walkability under ordinal encodings ($m{=}3$). Compression metrics corroborate these patterns: dictionary compressors agree on algorithmic redundancy, while normalized LZ78 captures statistical novelty. Socioeconomic variables display cross-sectional heterogeneity and near-memoryless dynamics, whereas spatial interaction induces bounded temporal memory and recurrent regimes. The framework thus distinguishes semantic organization (predictive causation and memory) from syntactic simplicity (description length) and clarifies how emergence manifests at different system layers. It is demonstrated on a caregiver--elder case study with dyad-level $\varepsilon$-machine reconstructions and compression-based diagnostics.
Personalized Learning Path Planning with Goal-Driven Learner State Modeling
Venue: arXiv cs.AI 2026
Year: 2026

Personalized Learning Path Planning (PLPP) aims to design adaptive learning paths that align with individual goals. While large language models (LLMs) show potential in personalizing learning experiences, existing approaches often lack mechanisms for goal-aligned planning. We introduce Pxplore, a novel framework for PLPP that integrates a reinforcement-based training paradigm and an LLM-driven educational architecture. We design a structured learner state model and an automated reward function that transforms abstract objectives into computable signals. We train the policy combining supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO), and deploy it within a real-world learning platform. Extensive experiments validate Pxplore's effectiveness in producing coherent, personalized, and goal-driven learning paths. We release our code and dataset at https://github.com/Pxplore/pxplore-algo.
Sparse Subnetwork Enhancement for Underrepresented Languages in Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) exhibit substantial performance disparities across languages, particularly between high- and low-resource settings. We propose a framework for improving performance in underrepresented languages while preserving general-purpose capabilities via targeted fine-tuning of sparse, language-associated subnetworks. Our approach identifies language-relevant neurons using Language Activation Probability Entropy (LAPE), an information-theoretic metric that reliably captures language-specific activation patterns, and fine-tunes only the corresponding weights. Experiments on Llama-3.1-8B, Mistral-Nemo-12B, and Aya-Expanse-8B across 12 mid- and low-resource languages show that our method consistently outperforms full fine-tuning, FFN-only fine-tuning, LoRA, IA^3, and random-subset baselines while updating only 0.2-1% of model parameters. We further show that sparse, neuron-targeted fine-tuning can inject new language capabilities without catastrophic forgetting, with potential applicability to other model capabilities. Mechanistic analyses of weight updates and internal representations reveal asymmetric roles of FFN projections in language adaptation and improved cross-lingual alignment. Finally, we release language neuron sets for over 100 languages together with our adaptation pipeline, enabling a cost-effective path for extending LLMs to underrepresented languages.
Active Tactile Exploration for Rigid Body Pose and Shape Estimation
Venue: arXiv cs.RO 2026
Year: 2026

General robot manipulation requires the handling of previously unseen objects. Learning a physically accurate model at test time can provide significant benefits in data efficiency, predictability, and reuse between tasks. Tactile sensing can compliment vision with its robustness to occlusion, but its temporal sparsity necessitates careful online exploration to maintain data efficiency. Direct contact can also cause an unrestrained object to move, requiring both shape and location estimation. In this work, we propose a learning and exploration framework that uses only tactile data to simultaneously determine the shape and location of rigid objects with minimal robot motion. We build on recent advances in contact-rich system identification to formulate a loss function that penalizes physical constraint violation without introducing the numerical stiffness inherent in rigid-body contact. Optimizing this loss, we can learn cuboid and convex polyhedral geometries with less than 10s of randomly collected data after first contact. Our exploration scheme seeks to maximize Expected Information Gain and results in significantly faster learning in both simulated and real-robot experiments. More information can be found at https://dairlab.github.io/activetactile